Chronic disease management is the biggest health care problem facing the United States today. Chronic diseases especially affect older adults, 80% of older adults have at least one chronic condition and 50% have two or more (CDC, 2007). The primary goal of chronic disease management is controlling disease rather than curing it. Early illness detection and recognition of small changes in health conditions are essential for early interventions when treatment is the most effective and when prevention of dramatic changes are still possible. Early illness recognition and early treatment is not only a key to improving health status with rapid recovery after an acute illness or exacerbation of a chronic illness, but also a key to reducing morbidity and mortality in older adults. Building on our current work using intelligent sensor systems to retrospectively measure functional ability in older adults, we propose to develop a prospective innovative technological approach to early illness detection and chronic disease management using inexpensive sensors embedded in the environment. Subjects will not use any expensive telehealth equipment or wear any devices. Instead, sensor data will be collected passively, thus eliminating compliance issues. In addition, the sensors monitor subjects continuously (motion sensors) while they go about daily activities in their homes. Unobtrusive bed sensors collect data about the subjects pulse, breathing, and restlessness while they sleep. We propose to use this information to detect changes in health status which could indicate an impending acute illness or exacerbation of chronic illness. Specifically, we propose to 1) develop an early illness sensor system that uses sensor data to detect early signs of illness or functional decline in older adults. We will further develop and refine a web-based interface to display sensor data in a format that health care providers find easy to use and interpret, readily available, and clinically relevant. We will develop alerts based on the sensor data and notify health care providers of potential illness in older adults so they can further evaluate and intervene with early treatment of acute illness or exacerbation of chronic illness. Then, we will prospectively use the early illness sensor system in a pilot study to 2) determine the sample size for an intervention study using the early illness sensor system in elder housing to measure the clinical effectiveness and cost-effectiveness of using sensor data to detect early signs of illness or functional decline in older adults as compared to usual health assessment. This application will be of interest to both NINR and NIA.
Project Narrative Building on our current work using intelligent sensor systems to retrospectively measure functional ability in older adults, we propose to develop a prospective innovative technological approach to early illness detection and chronic disease management using inexpensive sensors embedded in the environment. Subjects will not use any expensive telehealth equipment or wear any devices. We propose to use this information to detect changes in health status which could indicate an impending acute illness or exacerbation of chronic illness.
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